
from IPython.display import Image, display
image_url = "https://images.news18.com/ibnlive/uploads/2019/04/Sachin-Tendulkar-PTI.jpg?impolicy=website&width=510&height=356"
display(Image(url=image_url))


import os
os.getcwd()
os.chdir("D:\python")


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go


import warnings
warnings.filterwarnings('ignore')


df = pd.read_excel("tendulkar_centuries.xlsx")


df.head()


df.tail()


df.shape

(100, 12)


df.columns

Index(['Score', 'Out/Not Out', 'Against', 'Batting Order', 'Inn.',
       'Strike Rate', 'Venue', 'Coulmn 1', 'H/A', 'Date', 'Result', 'Format'],
      dtype='object')


df.duplicated().sum()

0


df.isnull().sum()

Score            0
Out/Not Out      0
Against          0
Batting Order    0
Inn.             0
Strike Rate      0
Venue            0
Coulmn 1         0
H/A              0
Date             0
Result           0
Format           0
dtype: int64


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 12 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Score          100 non-null    int64         
 1   Out/Not Out    100 non-null    object        
 2   Against        100 non-null    object        
 3   Batting Order  100 non-null    int64         
 4   Inn.           100 non-null    int64         
 5   Strike Rate    100 non-null    float64       
 6   Venue          100 non-null    object        
 7   Coulmn 1       100 non-null    object        
 8   H/A            100 non-null    object        
 9   Date           100 non-null    datetime64[ns]
 10  Result         100 non-null    object        
 11  Format         100 non-null    object        
dtypes: datetime64[ns](1), float64(1), int64(3), object(7)
memory usage: 9.5+ KB


mean_strike_rate = df['Strike Rate'].mean()
df['Strike Rate'].fillna(mean_strike_rate, inplace=True)


df.describe()


new_column_names = {
 'Score': 'Batting Score',
 'Out/Not Out': 'Batting Status',
 'Against': 'Opponent Team',
 'Batting Order': 'Batting Position',
 'Inn.': 'Inning Number',
 'Strike Rate': 'Batting Strike Rate',
 'Venue': 'Match Venue',
 'Column1': 'City',
 'H/A': 'Home/Away',
 'Date': 'Match Date',
 'Result': 'Match Result',
 'Format': 'Match Format'
}


df.rename(columns=new_column_names, inplace=True)


df.nunique()

Batting Score           58
Batting Status           2
Opponent Team           11
Batting Position         5
Inning Number            4
Batting Strike Rate     54
Match Venue             58
Coulmn 1                51
Home/Away                3
Match Date             100
Match Result             5
Match Format             2
dtype: int64


column_data_types = df.dtypes


categorical_columns = column_data_types[column_data_types == 'object'].index.tolist()
numerical_columns = column_data_types[column_data_types != 'object'].index.tolist()
print("Categorical Columns:", categorical_columns)
print()
print("Numerical Columns:", numerical_columns)

Categorical Columns: ['Batting Status', 'Opponent Team', 'Match Venue', 'Coulmn 1', 'Home/Away', 'Match Result', 'Match Format']

Numerical Columns: ['Batting Score', 'Batting Position', 'Inning Number', 'Batting Strike Rate', 'Match Date']


for i in categorical_columns:
 print(i, '- unique values are:')
 print(df[i].unique())
 print()

Batting Status - unique values are:
['Not Out' 'Out']

Opponent Team - unique values are:
['England' 'Australia' 'South Africa' 'Sri Lanka' 'New Zealand'
 'West Indies' 'Kenya' 'Pakistan' 'Zimbabwe' 'Namibia' 'Bangladesh']

Match Venue - unique values are:
['Old Trafford' 'Sydney Cricket Ground' 'WACA Ground' 'Wanderers Stadium'
 'M. A. Chidambaram Stadium' 'Sinhalese Sports Club Ground'
 'K. D. Singh Babu Stadium' 'R. Premadasa Stadium'
 'IPCL Sports Complex Ground' 'Sawai Mansingh Stadium'
 'Vidarbha Cricket Association Ground'
 'Sharjah Cricket Association Stadium' 'Barabati Stadium'
 'Feroz Shah Kotla' 'Padang' 'Edgbaston' 'Trent Bridge' 'Wankhede Stadium'
 'Newlands Cricket Ground' 'Willowmoore Park' 'M. Chinnaswamy Stadium'
 'Green Park Stadium' 'Eden Gardens' 'Queens Sports Club'
 'Bangabandhu Stadium' 'Basin Reserve' 'County Ground'
 'Punjab Cricket Association Stadium' 'Sardar Patel Stadium'
 'Lal Bahadur Shastri Stadium' 'Melbourne Cricket Ground'
 'Feroz Shah Kotla Ground' 'Barkatullah Khan Stadium' 'Nehru Stadium'
 'Harare Sports Club' 'New Wanderers Stadium' 'Boland Park'
 'Goodyear Park' "Queen's Park Oval" 'Riverside Ground' 'Headingley'
 'City Oval' 'Roop Singh Stadium' 'Rawalpindi Cricket Stadium'
 'Multan Cricket Stadium' 'Bangabandhu National Stadium'
 'Arbab Niaz Stadium' 'Kinrara Academy Oval'
 'Bir Shrestha Shahid Ruhul Amin Stadium' 'Sher-e-Bangla National Stadium'
 'Adelaide Oval' 'Vidarbha Cricket Association Stadium' 'AMI Stadium'
 'Seddon Park' 'Rajiv Gandhi International Stadium'
 'Zohur Ahmed Chowdhury Stadium' 'SuperSport Park' 'VCA Stadium']

Coulmn 1 - unique values are:
[' Manchester' ' Sydney' ' Perth' ' Johannesburg' ' Chennai' ' Colombo'
 ' Lucknow' ' Vadodara' ' Jaipur' ' Nagpur' ' Sharjah' ' Cuttack'
 ' New Delhi' ' Singapore' ' Birmingham' ' Nottingham' ' Mumbai'
 ' Cape Town' ' Benoni' ' Bangalore' ' Kanpur' ' Kolkata' ' Bulawayo'
 ' Dhaka' ' Wellington' ' Bristol' ' Mohali' 'Ahmedabad' ' Hyderabad'
 ' Melbourne' ' Jodhpur' ' Indore' ' Harare' ' Paarl' ' Bloemfontein'
 ' Ahmedabad' ' Port of Spain' ' Chester-le-Street' ' Leeds'
 ' Pietermaritzburg' ' Gwalior' ' Rawalpindi' ' Multan' ' Peshawar'
 ' Kuala Lumpur' ' Chittagong' ' Mirpur' ' Adelaide' ' Christchurch'
 ' Hamilton' ' Centurion']

Home/Away - unique values are:
['Away' 'Home' 'Neutral']


for i in categorical_columns:
 print(i, '- value counts are:')
 print(df[i].value_counts())
 print()

Batting Status - value counts are:
Out        69
Not Out    31
Name: Batting Status, dtype: int64

Opponent Team - value counts are:
Australia       20
Sri Lanka       17
South Africa    12
England          9
New Zealand      9
Zimbabwe         8
West Indies      7
Pakistan         7
Bangladesh       6
Kenya            4
Namibia          1
Name: Opponent Team, dtype: int64

Match Venue - value counts are:
Sharjah Cricket Association Stadium       7
M. A. Chidambaram Stadium                 5
Sinhalese Sports Club Ground              5
R. Premadasa Stadium                      5
Sydney Cricket Ground                     4
Sardar Patel Stadium                      4
M. Chinnaswamy Stadium                    4
IPCL Sports Complex Ground                3
Vidarbha Cricket Association Ground       3
Eden Gardens                              3
Sher-e-Bangla National Stadium            3
Lal Bahadur Shastri Stadium               2
Wankhede Stadium                          2
Feroz Shah Kotla Ground                   2
Roop Singh Stadium                        2
County Ground                             2
Newlands Cricket Ground                   2
Vidarbha Cricket Association Stadium      2
Bir Shrestha Shahid Ruhul Amin Stadium    1
Seddon Park                               1
Queen's Park Oval                         1
Riverside Ground                          1
Headingley                                1
SuperSport Park                           1
City Oval                                 1
Zohur Ahmed Chowdhury Stadium             1
Rajiv Gandhi International Stadium        1
AMI Stadium                               1
Boland Park                               1
Rawalpindi Cricket Stadium                1
Multan Cricket Stadium                    1
Bangabandhu National Stadium              1
Arbab Niaz Stadium                        1
Adelaide Oval                             1
Kinrara Academy Oval                      1
Goodyear Park                             1
Old Trafford                              1
New Wanderers Stadium                     1
Trent Bridge                              1
WACA Ground                               1
Wanderers Stadium                         1
K. D. Singh Babu Stadium                  1
Sawai Mansingh Stadium                    1
Barabati Stadium                          1
Feroz Shah Kotla                          1
Padang                                    1
Edgbaston                                 1
Willowmoore Park                          1
Harare Sports Club                        1
Green Park Stadium                        1
Queens Sports Club                        1
Bangabandhu Stadium                       1
Basin Reserve                             1
Punjab Cricket Association Stadium        1
Melbourne Cricket Ground                  1
Barkatullah Khan Stadium                  1
Nehru Stadium                             1
VCA Stadium                               1
Name: Match Venue, dtype: int64

Coulmn 1 - value counts are:
 Colombo              10
 Sharjah               7
 Nagpur                6
 Chennai               5
 Sydney                4
 Bangalore             4
 Vadodara              3
 Hyderabad             3
 Mirpur                3
 New Delhi             3
 Kolkata               3
Ahmedabad              2
 Cape Town             2
 Ahmedabad             2
 Dhaka                 2
 Gwalior               2
 Bristol               2
 Mumbai                2
 Chittagong            2
 Johannesburg          2
 Multan                1
 Paarl                 1
 Rawalpindi            1
 Peshawar              1
 Kuala Lumpur          1
 Pietermaritzburg      1
 Adelaide              1
 Leeds                 1
 Christchurch          1
 Chester-le-Street     1
 Port of Spain         1
 Hamilton              1
 Bloemfontein          1
 Manchester            1
 Harare                1
 Nottingham            1
 Perth                 1
 Lucknow               1
 Jaipur                1
 Cuttack               1
 Singapore             1
 Birmingham            1
 Benoni                1
 Indore                1
 Kanpur                1
 Bulawayo              1
 Wellington            1
 Mohali                1
 Melbourne             1
 Jodhpur               1
 Centurion             1
Name: Coulmn 1, dtype: int64

Home/Away - value counts are:
Home       42
Away       41
Neutral    17
Name: Home/Away, dtype: int64

Match Result - value counts are:
Won          53
Lost         25
Drawn        20
No result     1
Tied          1
Name: Match Result, dtype: int64

Match Format - value counts are:
Test    51
ODI     49
Name: Match Format, dtype: int64


import matplotlib.pyplot as plt
import seaborn as sns

for i in categorical_columns:
    if i != 'Match Date':
        print(i, '- Countplot:')
        plt.figure(figsize=(15, 6))
        sns.countplot(data=df, x=i, palette='hls')
        plt.xticks(rotation=90)
        plt.show()

Batting Status - Countplot:

Opponent Team - Countplot:

Match Venue - Countplot:

Coulmn 1 - Countplot:

Home/Away - Countplot:

Match Result - Countplot:

Match Format - Countplot:


import matplotlib.pyplot as plt
import seaborn as sns

for i in categorical_columns:
    if i != 'Match Date':
        print(i, '- Pieplot:')
        plt.figure(figsize=(10, 10))
        counts = df[i].value_counts()
        plt.pie(counts, labels=counts.index, autopct='%1.1f%%', colors=sns.color_palette())
        plt.title(i)
        plt.show()

Batting Status - Pieplot:

Opponent Team - Pieplot:

Match Venue - Pieplot:

Coulmn 1 - Pieplot:

Home/Away - Pieplot:

Match Result - Pieplot:

Match Format - Pieplot:


import plotly.graph_objs as go
from plotly.subplots import make_subplots

for i in categorical_columns:
    if i != 'Match Date':
        fig = make_subplots(rows=1, cols=1)  # Create a subplot
        fig.add_trace(go.Bar(x=df[i].value_counts().index, y=df[i].value_counts()))
        fig.update_layout(title=i, xaxis_title="Categories", yaxis_title="Count")
        fig.show()


import plotly.graph_objs as go

for i in categorical_columns:
    if i != 'Match Date':
        counts = df[i].value_counts()
        fig = go.Figure(data=[go.Pie(labels=counts.index, values=counts)])
        fig.update_layout(title=i)
        fig.show()


for i in numerical_columns:
 plt.figure(figsize=(15, 6))
 sns.histplot(df[i], kde=True, bins=20, palette='hls')
 plt.xticks(rotation=90)
 plt.show()


for i in numerical_columns:
 plt.figure(figsize=(15, 6))
 sns.distplot(df[i], kde = True, bins = 20)
 plt.xticks(rotation=90)
 plt.show()


for i in numerical_columns:
 plt.figure(figsize=(15, 6))
 sns.boxplot(df[i], data = df, palette='hls')
 plt.xticks(rotation=90)
 plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_20704\2823074623.py in <module>
      1 for i in numerical_columns:
      2  plt.figure(figsize=(15, 6))
----> 3  sns.boxplot(df[i], data = df, palette='hls')
      4  plt.xticks(rotation=90)
      5  plt.show()

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\categorical.py in boxplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)
   2241 ):
   2242 
-> 2243     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,
   2244                           orient, color, palette, saturation,
   2245                           width, dodge, fliersize, linewidth)

~\anaconda3\lib\site-packages\seaborn\categorical.py in __init__(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)
    404                  width, dodge, fliersize, linewidth):
    405 
--> 406         self.establish_variables(x, y, hue, data, orient, order, hue_order)
    407         self.establish_colors(color, palette, saturation)
    408 

~\anaconda3\lib\site-packages\seaborn\categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
    154 
    155             # Figure out the plotting orientation
--> 156             orient = infer_orient(
    157                 x, y, orient, require_numeric=self.require_numeric
    158             )

~\anaconda3\lib\site-packages\seaborn\_core.py in infer_orient(x, y, orient, require_numeric)
   1326             warnings.warn(single_var_warning.format("Vertical", "x"))
   1327         if require_numeric and x_type != "numeric":
-> 1328             raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
   1329         return "h"
   1330 

TypeError: Horizontal orientation requires numeric `x` variable.

<Figure size 1500x600 with 0 Axes>


for i in numerical_columns:
 plt.figure(figsize=(15, 6))
 sns.violinplot(df[i], data = df, palette='hls')
 plt.xticks(rotation=90)
 plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_20704\920855186.py in <module>
      1 for i in numerical_columns:
      2  plt.figure(figsize=(15, 6))
----> 3  sns.violinplot(df[i], data = df, palette='hls')
      4  plt.xticks(rotation=90)
      5  plt.show()

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\categorical.py in violinplot(x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation, ax, **kwargs)
   2398 ):
   2399 
-> 2400     plotter = _ViolinPlotter(x, y, hue, data, order, hue_order,
   2401                              bw, cut, scale, scale_hue, gridsize,
   2402                              width, inner, split, dodge, orient, linewidth,

~\anaconda3\lib\site-packages\seaborn\categorical.py in __init__(self, x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation)
    520                  color, palette, saturation):
    521 
--> 522         self.establish_variables(x, y, hue, data, orient, order, hue_order)
    523         self.establish_colors(color, palette, saturation)
    524         self.estimate_densities(bw, cut, scale, scale_hue, gridsize)

~\anaconda3\lib\site-packages\seaborn\categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
    154 
    155             # Figure out the plotting orientation
--> 156             orient = infer_orient(
    157                 x, y, orient, require_numeric=self.require_numeric
    158             )

~\anaconda3\lib\site-packages\seaborn\_core.py in infer_orient(x, y, orient, require_numeric)
   1326             warnings.warn(single_var_warning.format("Vertical", "x"))
   1327         if require_numeric and x_type != "numeric":
-> 1328             raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
   1329         return "h"
   1330 

TypeError: Horizontal orientation requires numeric `x` variable.

<Figure size 1500x600 with 0 Axes>


import plotly.graph_objs as go

for col in numerical_columns:
    fig = go.Figure()
    fig.add_trace(go.Histogram(x=df[col], nbinsx=20, name=col, opacity=0.7))
    fig.update_layout(
        title=f'{col} - Histogram with Bar Lines',
        xaxis_title=col,
        yaxis_title='Frequency'
    )
    fig.show()


for col in numerical_columns:
 fig = px.box(df, y=col, title=f'{col} - Box Plot')
 fig.show()


for col in numerical_columns:
 fig = px.violin(df, y=col, title=f'{col} - Box Plot')
 fig.show()


for i in numerical_columns:
    for j in categorical_columns:
        if j != 'Match Date':
            plt.figure(figsize=(15, 6))
            sns.barplot(x=df[j], y=df[i], ci=None, data=df, palette='hls')
            plt.xticks(rotation=90)
            plt.title(f'{i} vs {j} Barplot')
            plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_20704\567915536.py in <module>
      3         if j != 'Match Date':
      4             plt.figure(figsize=(15, 6))
----> 5             sns.barplot(x=df[j], y=df[i], ci=None, data=df, palette='hls')
      6             plt.xticks(rotation=90)
      7             plt.title(f'{i} vs {j} Barplot')

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\categorical.py in barplot(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge, ax, **kwargs)
   3180 ):
   3181 
-> 3182     plotter = _BarPlotter(x, y, hue, data, order, hue_order,
   3183                           estimator, ci, n_boot, units, seed,
   3184                           orient, color, palette, saturation,

~\anaconda3\lib\site-packages\seaborn\categorical.py in __init__(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)
   1582                  errwidth, capsize, dodge):
   1583         """Initialize the plotter."""
-> 1584         self.establish_variables(x, y, hue, data, orient,
   1585                                  order, hue_order, units)
   1586         self.establish_colors(color, palette, saturation)

~\anaconda3\lib\site-packages\seaborn\categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
    154 
    155             # Figure out the plotting orientation
--> 156             orient = infer_orient(
    157                 x, y, orient, require_numeric=self.require_numeric
    158             )

~\anaconda3\lib\site-packages\seaborn\_core.py in infer_orient(x, y, orient, require_numeric)
   1350     elif require_numeric and "numeric" not in (x_type, y_type):
   1351         err = "Neither the `x` nor `y` variable appears to be numeric."
-> 1352         raise TypeError(err)
   1353 
   1354     else:

TypeError: Neither the `x` nor `y` variable appears to be numeric.

<Figure size 1500x600 with 0 Axes>


for i in numerical_columns:
 for j in categorical_columns:
 if j != 'Match Date':
 plt.figure(figsize=(15, 6))
 sns.boxplot(x = df[j], y = df[i], data = df, palette='hls')
 plt.xticks(rotation=90)
 plt.show()


for i in numerical_columns:
    for j in categorical_columns:
        if j != 'Match Date':
            plt.figure(figsize=(15, 6))
            sns.boxplot(x=df[j], y=df[i], data=df, palette='hls')
            plt.xticks(rotation=90)
            plt.title(f'{i} vs {j} Boxplot')
            plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_20704\4140272649.py in <module>
      3         if j != 'Match Date':
      4             plt.figure(figsize=(15, 6))
----> 5             sns.boxplot(x=df[j], y=df[i], data=df, palette='hls')
      6             plt.xticks(rotation=90)
      7             plt.title(f'{i} vs {j} Boxplot')

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\categorical.py in boxplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)
   2241 ):
   2242 
-> 2243     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,
   2244                           orient, color, palette, saturation,
   2245                           width, dodge, fliersize, linewidth)

~\anaconda3\lib\site-packages\seaborn\categorical.py in __init__(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)
    404                  width, dodge, fliersize, linewidth):
    405 
--> 406         self.establish_variables(x, y, hue, data, orient, order, hue_order)
    407         self.establish_colors(color, palette, saturation)
    408 

~\anaconda3\lib\site-packages\seaborn\categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
    154 
    155             # Figure out the plotting orientation
--> 156             orient = infer_orient(
    157                 x, y, orient, require_numeric=self.require_numeric
    158             )

~\anaconda3\lib\site-packages\seaborn\_core.py in infer_orient(x, y, orient, require_numeric)
   1350     elif require_numeric and "numeric" not in (x_type, y_type):
   1351         err = "Neither the `x` nor `y` variable appears to be numeric."
-> 1352         raise TypeError(err)
   1353 
   1354     else:

TypeError: Neither the `x` nor `y` variable appears to be numeric.

<Figure size 1500x600 with 0 Axes>


for i in numerical_columns:
    for j in categorical_columns:
        if j != 'Match Date':
            plt.figure(figsize=(15, 6))
            sns.violinplot(x=df[j], y=df[i], data=df, palette='hls')
            plt.xticks(rotation=90)
            plt.title(f'{i} vs {j} Violinplot')
            plt.show()


for i in numerical_columns:
 for j in categorical_columns:
 fig = px.bar(df, x=j, y=i, title=f'{i} vs {j} - Bar Plot', labels={j: 'Category'
 fig.show()


import plotly.express as px

for i in numerical_columns:
    for j in categorical_columns:
        fig = px.bar(df, x=j, y=i, title=f'{i} vs {j} - Bar Plot', labels={j: 'Category', i: 'Count'})
        fig.show()


import plotly.express as px

for i in numerical_columns:
    for j in categorical_columns:
        fig = px.box(df, x=j, y=i, title=f'{i} vs {j} - Box Plot', labels={j: 'Category', i: 'Value'})
        fig.show()


import plotly.express as px

for i in numerical_columns:
    for j in categorical_columns:
        fig = px.violin(df, x=j, y=i, title=f'{i} vs {j} - Violin Plot', labels={j: 'Category', i: 'Value'})
        fig.show()


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

for col1 in categorical_columns:
    for col2 in categorical_columns:
        if col1 != col2:
            crosstab_result = pd.crosstab(df[col1], df[col2])
            print(f"Crosstab Analysis between '{col1}' and '{col2}':\n")
            print(crosstab_result)
            print("\n")
            plt.figure(figsize=(10, 6))
            sns.heatmap(crosstab_result, annot=True, cmap="YlGnBu", fmt='d')
            plt.title(f"Heatmap: Crosstab Analysis between '{col1}' and '{col2}'")
            plt.xlabel(col2)
            plt.ylabel(col1)
            plt.show()

Crosstab Analysis between 'Batting Status' and 'Opponent Team':

Opponent Team   Australia  Bangladesh  England  Kenya  Namibia  New Zealand  \
Batting Status                                                                
Not Out                 5           3        3      3        0            3   
Out                    15           3        6      1        1            6   

Opponent Team   Pakistan  South Africa  Sri Lanka  West Indies  Zimbabwe  
Batting Status                                                            
Not Out                1             2          4            3         4  
Out                    6            10         13            4         4

Crosstab Analysis between 'Batting Status' and 'Match Venue':

Match Venue     AMI Stadium  Adelaide Oval  Arbab Niaz Stadium  \
Batting Status                                                   
Not Out                   1              0                   0   
Out                       0              1                   1   

Match Venue     Bangabandhu National Stadium  Bangabandhu Stadium  \
Batting Status                                                      
Not Out                                    1                    0   
Out                                        0                    1   

Match Venue     Barabati Stadium  Barkatullah Khan Stadium  Basin Reserve  \
Batting Status                                                              
Not Out                        1                         0              0   
Out                            0                         1              1   

Match Venue     Bir Shrestha Shahid Ruhul Amin Stadium  Boland Park  ...  \
Batting Status                                                       ...   
Not Out                                              0            0  ...   
Out                                                  1            1  ...   

Match Venue     Sydney Cricket Ground  Trent Bridge  VCA Stadium  \
Batting Status                                                     
Not Out                             4             0            0   
Out                                 0             1            1   

Match Venue     Vidarbha Cricket Association Ground  \
Batting Status                                        
Not Out                                           1   
Out                                               2   

Match Venue     Vidarbha Cricket Association Stadium  WACA Ground  \
Batting Status                                                      
Not Out                                            0            0   
Out                                                2            1   

Match Venue     Wanderers Stadium  Wankhede Stadium  Willowmoore Park  \
Batting Status                                                          
Not Out                         0                 0                 0   
Out                             1                 2                 1   

Match Venue     Zohur Ahmed Chowdhury Stadium  
Batting Status                                 
Not Out                                     1  
Out                                         0  

[2 rows x 58 columns]

Crosstab Analysis between 'Batting Status' and 'Coulmn 1':

Coulmn 1         Adelaide   Ahmedabad   Bangalore   Benoni   Birmingham  \
Batting Status                                                            
Not Out                 0           1           0        0            0   
Out                     1           1           4        1            1   

Coulmn 1         Bloemfontein   Bristol   Bulawayo   Cape Town   Centurion  \
Batting Status                                                               
Not Out                     0         1          1           0           1   
Out                         1         1          0           2           0   

Coulmn 1        ...   Peshawar   Pietermaritzburg   Port of Spain  \
Batting Status  ...                                                 
Not Out         ...          0                  0               0   
Out             ...          1                  1               1   

Coulmn 1         Rawalpindi   Sharjah   Singapore   Sydney   Vadodara  \
Batting Status                                                          
Not Out                   0         3           0        4          1   
Out                       1         4           1        0          2   

Coulmn 1         Wellington  Ahmedabad  
Batting Status                          
Not Out                   0          0  
Out                       1          2  

[2 rows x 51 columns]

Crosstab Analysis between 'Batting Status' and 'Home/Away':

Home/Away       Away  Home  Neutral
Batting Status                     
Not Out           15    10        6
Out               26    32       11

Crosstab Analysis between 'Batting Status' and 'Match Result':

Match Result    Drawn  Lost  No result  Tied  Won
Batting Status                                   
Not Out             7     3          1     0   20
Out                13    22          0     1   33

Crosstab Analysis between 'Batting Status' and 'Match Format':

Match Format    ODI  Test
Batting Status           
Not Out          15    16
Out              34    35

Crosstab Analysis between 'Opponent Team' and 'Batting Status':

Batting Status  Not Out  Out
Opponent Team               
Australia             5   15
Bangladesh            3    3
England               3    6
Kenya                 3    1
Namibia               0    1
New Zealand           3    6
Pakistan              1    6
South Africa          2   10
Sri Lanka             4   13
West Indies           3    4
Zimbabwe              4    4

Crosstab Analysis between 'Opponent Team' and 'Match Venue':

Match Venue    AMI Stadium  Adelaide Oval  Arbab Niaz Stadium  \
Opponent Team                                                   
Australia                0              1                   0   
Bangladesh               0              0                   0   
England                  0              0                   0   
Kenya                    0              0                   0   
Namibia                  0              0                   0   
New Zealand              1              0                   0   
Pakistan                 0              0                   1   
South Africa             0              0                   0   
Sri Lanka                0              0                   0   
West Indies              0              0                   0   
Zimbabwe                 0              0                   0   

Match Venue    Bangabandhu National Stadium  Bangabandhu Stadium  \
Opponent Team                                                      
Australia                                 0                    1   
Bangladesh                                1                    0   
England                                   0                    0   
Kenya                                     0                    0   
Namibia                                   0                    0   
New Zealand                               0                    0   
Pakistan                                  0                    0   
South Africa                              0                    0   
Sri Lanka                                 0                    0   
West Indies                               0                    0   
Zimbabwe                                  0                    0   

Match Venue    Barabati Stadium  Barkatullah Khan Stadium  Basin Reserve  \
Opponent Team                                                              
Australia                     0                         0              0   
Bangladesh                    0                         0              0   
England                       0                         0              0   
Kenya                         1                         0              0   
Namibia                       0                         0              0   
New Zealand                   0                         0              1   
Pakistan                      0                         0              0   
South Africa                  0                         0              0   
Sri Lanka                     0                         0              0   
West Indies                   0                         0              0   
Zimbabwe                      0                         1              0   

Match Venue    Bir Shrestha Shahid Ruhul Amin Stadium  Boland Park  ...  \
Opponent Team                                                       ...   
Australia                                           0            0  ...   
Bangladesh                                          1            0  ...   
England                                             0            0  ...   
Kenya                                               0            1  ...   
Namibia                                             0            0  ...   
New Zealand                                         0            0  ...   
Pakistan                                            0            0  ...   
South Africa                                        0            0  ...   
Sri Lanka                                           0            0  ...   
West Indies                                         0            0  ...   
Zimbabwe                                            0            0  ...   

Match Venue    Sydney Cricket Ground  Trent Bridge  VCA Stadium  \
Opponent Team                                                     
Australia                          4             0            0   
Bangladesh                         0             0            0   
England                            0             1            0   
Kenya                              0             0            0   
Namibia                            0             0            0   
New Zealand                        0             0            0   
Pakistan                           0             0            0   
South Africa                       0             0            1   
Sri Lanka                          0             0            0   
West Indies                        0             0            0   
Zimbabwe                           0             0            0   

Match Venue    Vidarbha Cricket Association Ground  \
Opponent Team                                        
Australia                                        0   
Bangladesh                                       0   
England                                          0   
Kenya                                            0   
Namibia                                          0   
New Zealand                                      0   
Pakistan                                         0   
South Africa                                     0   
Sri Lanka                                        0   
West Indies                                      1   
Zimbabwe                                         2   

Match Venue    Vidarbha Cricket Association Stadium  WACA Ground  \
Opponent Team                                                      
Australia                                         1            1   
Bangladesh                                        0            0   
England                                           0            0   
Kenya                                             0            0   
Namibia                                           0            0   
New Zealand                                       0            0   
Pakistan                                          0            0   
South Africa                                      1            0   
Sri Lanka                                         0            0   
West Indies                                       0            0   
Zimbabwe                                          0            0   

Match Venue    Wanderers Stadium  Wankhede Stadium  Willowmoore Park  \
Opponent Team                                                          
Australia                      0                 0                 0   
Bangladesh                     0                 0                 0   
England                        0                 0                 0   
Kenya                          0                 0                 0   
Namibia                        0                 0                 0   
New Zealand                    0                 0                 0   
Pakistan                       0                 0                 0   
South Africa                   1                 1                 0   
Sri Lanka                      0                 1                 0   
West Indies                    0                 0                 0   
Zimbabwe                       0                 0                 1   

Match Venue    Zohur Ahmed Chowdhury Stadium  
Opponent Team                                 
Australia                                  0  
Bangladesh                                 1  
England                                    0  
Kenya                                      0  
Namibia                                    0  
New Zealand                                0  
Pakistan                                   0  
South Africa                               0  
Sri Lanka                                  0  
West Indies                                0  
Zimbabwe                                   0  

[11 rows x 58 columns]

Crosstab Analysis between 'Opponent Team' and 'Coulmn 1':

Coulmn 1        Adelaide   Ahmedabad   Bangalore   Benoni   Birmingham  \
Opponent Team                                                            
Australia              1           0           2        0            0   
Bangladesh             0           0           0        0            0   
England                0           1           1        0            1   
Kenya                  0           0           0        0            0   
Namibia                0           0           0        0            0   
New Zealand            0           0           1        0            0   
Pakistan               0           0           0        0            0   
South Africa           0           0           0        0            0   
Sri Lanka              0           1           0        0            0   
West Indies            0           0           0        0            0   
Zimbabwe               0           0           0        1            0   

Coulmn 1        Bloemfontein   Bristol   Bulawayo   Cape Town   Centurion  \
Opponent Team                                                               
Australia                  0         0          0           0           0   
Bangladesh                 0         0          0           0           0   
England                    0         0          0           0           0   
Kenya                      0         1          0           0           0   
Namibia                    0         0          0           0           0   
New Zealand                0         0          0           0           0   
Pakistan                   0         0          0           0           0   
South Africa               1         0          0           2           1   
Sri Lanka                  0         1          0           0           0   
West Indies                0         0          0           0           0   
Zimbabwe                   0         0          1           0           0   

Coulmn 1       ...   Peshawar   Pietermaritzburg   Port of Spain   Rawalpindi  \
Opponent Team  ...                                                              
Australia      ...          0                  0               0            0   
Bangladesh     ...          0                  0               0            0   
England        ...          0                  0               0            0   
Kenya          ...          0                  0               0            0   
Namibia        ...          0                  1               0            0   
New Zealand    ...          0                  0               0            0   
Pakistan       ...          1                  0               0            1   
South Africa   ...          0                  0               0            0   
Sri Lanka      ...          0                  0               0            0   
West Indies    ...          0                  0               1            0   
Zimbabwe       ...          0                  0               0            0   

Coulmn 1        Sharjah   Singapore   Sydney   Vadodara   Wellington  \
Opponent Team                                                          
Australia             2           0        4          0            0   
Bangladesh            0           0        0          0            0   
England               0           0        0          0            0   
Kenya                 0           0        0          0            0   
Namibia               0           0        0          0            0   
New Zealand           0           0        0          1            1   
Pakistan              1           1        0          0            0   
South Africa          0           0        0          1            0   
Sri Lanka             2           0        0          0            0   
West Indies           0           0        0          1            0   
Zimbabwe              2           0        0          0            0   

Coulmn 1       Ahmedabad  
Opponent Team             
Australia              0  
Bangladesh             0  
England                0  
Kenya                  0  
Namibia                0  
New Zealand            1  
Pakistan               1  
South Africa           0  
Sri Lanka              0  
West Indies            0  
Zimbabwe               0  

[11 rows x 51 columns]

Crosstab Analysis between 'Opponent Team' and 'Home/Away':

Home/Away      Away  Home  Neutral
Opponent Team                     
Australia         7     9        4
Bangladesh        6     0        0
England           5     4        0
Kenya             0     2        2
Namibia           0     0        1
New Zealand       3     6        0
Pakistan          3     2        2
South Africa      6     6        0
Sri Lanka         9     5        3
West Indies       1     4        2
Zimbabwe          1     4        3


import pandas as pd
import plotly.express as px

for col1 in categorical_columns:
    for col2 in categorical_columns:
        if col1 != col2:
            crosstab_result = pd.crosstab(df[col1], df[col2])
            fig = px.imshow(
                crosstab_result,
                x=crosstab_result.columns,
                y=crosstab_result.index,
                color_continuous_scale="YlGnBu",
                title=f"Heatmap: Crosstab Analysis between '{col1}' and '{col2}'"
            )
            fig.update_xaxes(side="top")
            fig.show()


df['Match Date'] = pd.to_datetime(df['Match Date'])


df['Year'] = df['Match Date'].dt.year
df['Month'] = df['Match Date'].dt.month_name()


matches_per_year = df.groupby('Year').size().reset_index(name='Matches')
matches_per_month = df.groupby(['Year', 'Month']).size().reset_index(name='Matches')


fig_year = px.line(matches_per_year, x='Year', y='Matches', title='Matches per Year')
fig_year.show()


import plotly.express as px

for year in df['Year'].unique():
    matches_year = matches_per_month[matches_per_month['Year'] == year]
    fig = px.line(matches_year, x='Month', y='Matches', title=f'Matches per Month ({year})')
    fig.update_xaxes(type='category', categoryorder='category ascending')
    fig.show()


corr_matrix = df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix')
plt.show()


import plotly.express as px
import pandas as pd
inning_trends = df.groupby('Match Date')['Inning Number'].mean().reset_index()
fig_inning = px.line(inning_trends, x='Match Date', y='Inning Number', title='Trends Over Time')
fig_inning.show()


import plotly.express as px
import pandas as pd
score_trends = df.groupby('Match Date')['Batting Score'].mean().reset_index()
fig_score = px.line(score_trends, x='Match Date', y='Batting Score', title='Trends Over Time')
fig_score.show()

	Score	Batting Order	Inn.	Strike Rate
count	100.00000	100.000000	100.000000	100.000000
mean	135.57000	3.140000	1.630000	50.416900
std	33.33038	1.206213	0.786952	50.899145
min	100.00000	1.000000	1.000000	1.000000
25%	111.00000	2.000000	1.000000	2.000000
50%	124.00000	4.000000	1.000000	4.500000
75%	149.00000	4.000000	2.000000	97.692500
max	248.00000	6.000000	4.000000	138.610000

	Score	Out/Not Out	Against	Batting Order	Inn.	Strike Rate	Venue	Coulmn 1	H/A	Date	Result	Format
0	119	Not Out	England	6	4	2.0	Old Trafford	Manchester	Away	1990-08-09	Drawn	Test
1	148	Not Out	Australia	6	2	3.0	Sydney Cricket Ground	Sydney	Away	1992-01-02	Drawn	Test
2	114	Out	Australia	4	2	5.0	WACA Ground	Perth	Away	1992-02-01	Lost	Test
3	111	Out	South Africa	4	2	2.0	Wanderers Stadium	Johannesburg	Away	1992-11-26	Drawn	Test
4	165	Out	England	4	1	2.0	M. A. Chidambaram Stadium	Chennai	Home	1993-02-11	Won	Test

	Score	Out/Not Out	Against	Batting Order	Inn.	Strike Rate	Venue	Coulmn 1	H/A	Date	Result	Format
95	111	Not Out	South Africa	5	3	1.00	SuperSport Park	Centurion	Away	2010-12-16	Lost	Test
96	146	Out	South Africa	4	2	3.00	Newlands Cricket Ground	Cape Town	Away	2011-01-02	Drawn	Test
97	120	Out	England	2	1	104.34	M. Chinnaswamy Stadium	Bangalore	Home	2011-02-27	Tied	ODI
98	111	Out	South Africa	2	1	109.90	VCA Stadium	Nagpur	Home	2011-03-12	Lost	ODI
99	114	Out	Bangladesh	2	1	77.55	Sher-e-Bangla National Stadium	Mirpur	Away	2012-03-16	Lost	ODI